Intrinsic TGF-β signaling attenuates proximal tubule mitochondrial injury and inflammation in chronic kidney disease

Excessive TGF-β signaling and mitochondrial dysfunction fuel chronic kidney disease (CKD) progression. However, inhibiting TGF-β failed to impede CKD in humans. The proximal tubule (PT), the most vulnerable renal segment, is packed with giant mitochondria and injured PT is pivotal in CKD progression. How TGF-β signaling affects PT mitochondria in CKD remained unknown. Here, we combine spatial transcriptomics and bulk RNAseq with biochemical analyses to depict the role of TGF-β signaling on PT mitochondrial homeostasis and tubulo-interstitial interactions in CKD. Male mice carrying specific deletion of Tgfbr2 in the PT have increased mitochondrial injury and exacerbated Th1 immune response in the aristolochic acid model of CKD, partly, through impaired complex I expression and mitochondrial quality control associated with a metabolic rewiring toward aerobic glycolysis in the PT cells. Injured S3T2 PT cells are identified as the main mediators of the maladaptive macrophage/dendritic cell activation in the absence of Tgfbr2. snRNAseq database analyses confirm decreased TGF-β receptors and a metabolic deregulation in the PT of CKD patients. This study describes the role of TGF-β signaling in PT mitochondrial homeostasis and inflammation in CKD, suggesting potential therapeutic targets that might be used to mitigate CKD progression.

The main critique is the novelty of the question addressed. Others have examined mitochondrial dysfunction associated with TGF-beta in kidney disease. In facts, this topic has been the subject of multiple reviews, including a 2017 review in Kidney International (PMID: 28890325) and a 2012 review in Seminars in Nephrology (PMID: 22835461). However, most of the prior investigations lack the comprehensive approach of the work by Kayhan et. al or do not directly focus on the TGFBR2. One investigation in Disease models and mechanisms (PMID: 34431499) performs similar work investigating TGFB3 and another in plos one assesses the effects of TGFBR1 inhibition (31318905). The work by Kayhan et al is more comprehensive than either.
Specific critiques: 1.The abstract is general and fails to convey the experimentation conducted or results obtained. Humans and patients are mentioned in the abstract, but not mice, yet mouse and cell culture models constitute the bulk of the results.
2. Figure  In the manuscript titled "Intrinsic TGF-ß signaling attenuates proximal tubule mitochondrial injury and inflammation in chronic kidney disease", the authors built on their earlier work on TGF receptor signaling. Here, they investigated the interplay of aristolochic acid-mediated injury of the kidney and the targeted deletion of the Tgfbr2 receptor in the proximal tubules in mice now focusing on the consequences of Tgfbr2 receptor deletion on mitochondrial function and tissue inflammation status. In a second line of experiments, the authors complemented their studies in mice by investigating conditionally immortalized proximal tubule cells that lack the TßRII receptor. In a third line of experiments, the authors chose a spatial transcriptomics approach to identify cells in kidney sections by their transcript profiles and with that to gather spatiotemporal information on cellular and transcriptional changes following the injury by aristolochic acid (AA) in the Tgfbr2 receptor deletion mouse.
General comments: This study updates our current understanding of the adaptability of TGF-ß signaling by shedding light on its role in mitochondrial homeostasis and renal immunity not only in the context of the presented kidney injury model but also by pointing to similarities to the human CKD phenotype. Hence, due to the high prevalence of CKD in our society and the importance of TGF-ß signaling in this context, the manuscript will be of great interest to the clinical and research community.
The biochemical work involving conditionally immortalized proximal tubule cells is thoroughly and carefully carried out, documented, and presented. The same is the case for all histology and imaging experiments on mouse samples.
In contrast, the transcriptomic experiments fall somewhat short. The reviewer appreciates that the authors highlight shortcomings in this area in the discussion section of their manuscript. Particularly, the use of single cell RNA sequencing (scRNAseq) data that stem from a biological experiment unrelated to the one pursued here are a concern as they were used to identify cell types in the spatial transcriptomics data, which formed the base of subsequent analyses and experimental steps. However, the observations made by the authors were substantiated by independent approaches they had carried out to test their hypotheses. Still, the chosen approach raises concerns. It would have been ideal if the authors could have generated scRNAseq data of their mouse model and used that as a source of single cell information. However, it is understood that such experiments are very expensive and hence not easily accessible. Spatial transcriptomics is an up-and-coming technology that has a lot to offer, and the authors made the best of it in terms of what was available to them.
The transcriptomic experiments fall short for another reason. The manuscript provides only very sparse information on sample generation, source of data (no specifics to which of the data from GEO/Zenodo were used), and processing (how were the different datasets processed? How many cells went into the analysis? What percentage of doublets/mito/ribo transcripts were excluded? etc.). For example, it is not clear to the reviewer whether frozen or fixed samples were used for the Visium approach, what section thickness was chosen, how sections were selected etc. This technology is young and even less accessible to many laboratories. Hence, it is essential that details are provided at a level that is sufficient to allow the reader to understand the experimental design and to reproduce the data processing steps. With regards to reproducibility, it is now common practice to provide key steps of data processing and visualization on open-access platforms such as Github.
Minor comments: Title: The authors might want to limit the statement to the model they investigated i.e., AA mouse model of CDK. Alternatively, the authors may want to mention the use of a mouse model in the Abstract.
The reviewer felt at times frustrated about the frequent reference to 'data not shown' in the manuscript.
A bit more background information would be helpful to the non-initiated reader. For example, the AA model and the effects of AA on the kidney are not introduced. Not everybody is aware of the mito-QC mouse. What does it offer? Inconsistency when describing molecular players i.e., Pink1 is described but not LC3A-I.
There is a typo in line 275: "CD8+ cells were augmented in gGT-Cre; Tgfbr2fl/fl kidneys". It should read: "… augmented in Tgfbr2fl/fl kidneys." It is not clear what the number of animals was in the experiments that lead to panels 1b and 1c i.e., how variable are the proportions of cell types in the tested groups, and, as the study focuses on male mice, how representative are the findings in general? - Fig 1a: at the resolution provided, the text overlayed onto the UMAP is not legible and, in the case of Myo/St.mixed, seems not to be in the correct location - Fig 1b: The text overlayed on the spatial feature plot are not legible; the colors for S3 and Endo/Glom are hard to distinguish, especially with the H&E background. It is not clear which time point after aristolochic acid treatment is reflected here. -ED Fig5: typos in headers; the reviewer does not find the cartoon-like Metacore representation very helpful. In panel a, the 'meter' that shows the expression levels obscures some of the components it associates with; a table format would be easier to follow; however, the depiction of the pathways is appreciated.
• Figure 6 and ED (Extended Data) Figure 6: -ED Fig6: the image of the western blot is suggesting that the antibody is detecting two bands for PGc1alpha in TßRIIflox/flox; is that typical?  In response to the reviewers' comments, the following changes have been made:

Reviewer #1
In this manuscript, the authors show how TGF-β signaling affects mitochondrial quality control mechanisms, fibrosis, fatty acid metabolism, and inflammation in the proximal tubules and how these alterations attenuate mitochondrial dysfunction and other markers of chronic kidney disease. Overall, this is a very good and important paper. However, the manuscript is too dense and tries to cover too many issues. As such, the manuscript loses focus and data integrity. Finally, the Discussion overstates the stated relationships and conclusions.

Response:
We appreciate the reviewer's comment and constructive critiques. We made efforts to contract the text and improve the stated relationships and conclusions.
Below are some are some deficiencies and questions:  Figure 1j, BUN levels seem not to be different statistically Response: We thank the reviewer for the comments. The quantification was statistically significant regardless data distribution. However, we added 4 Tgfbr2 fl/fl and 3 GTCre;Tgfbr2 fl/fl mice to improve the statistical significance. Figures 3F, 3G, 3M, and 3N was not subjected to statistical analysis nor the conclusion Response: We thank the reviewer for pointing this remark out, and we understood that the reviewer actually referred to the Seahorse graphs in the "Extended Data Fig. 3" which has become Extended Data Fig. 4 in the revised version of the manuscript. The panels (f and g) are representative graphs of oxygen consumption rate (f) and extracellular acidification (g).

3)
The quantifications of relevant metabolic parameters are in the following panels (h-l). We added quantification of relevant metabolic parameters for panels (m and n) in the Supplementary Fig. 3 and 4. Figure 3b, densitometry analysis of ndufb8, sdhb, and mtco1 are shown. However, only ndufb8 is statistically reduced gGTCre;Tgfbr2fl/fl compared to Tgfbr2fl/fl. Please show densitometry of all complexes or only shown densitometry of the ones statistically different Response: We thank the reviewer for this comment. We included densitometry of sdhb and mtco1 to show their reduction trends in the GT-Cre;Tgfbr2 fl/fl mice compared to the floxed mice. However, we agree with the reviewer and removed densitometry analysis of other subunits in Figure 3.

5)
In figure 3c, only 4 complexes are shown in the immunoblot. If this immunoblot was performed still using antibody # ab110413, all 5 complexes should be shown. Is there a reason why the complex III band is not shown?

Response:
We thank the reviewer for this important remark and apologize for wrongly labelling complex III as complex IV in the panel c. We corrected ETC subunit labelling in panel c. Regarding complex IV subunit (MTCO1), its expression is weak in our PT cells (faint bands).
Moreover, the antibody manufacturer mentioned in the datasheet (*) that "the MTCO1 is highly hydrophobic. Its predicted MW is 57kDa but it differently migrates in different gel systems (https://www.abcam.com/total-oxphos-rodent-wb-antibody-cocktail-ab110413.html). However, MTCO1 bands with an apparent MW of approx. 35 kDa are visible after long exposure at higher exposure followed by image adjustment (red arrows). Please find below an example of gels where red arrows indicate faint MTCO1 bands.

6) Evidence support GAPDH is an unstable housekeeping gene in transcript level in ischemia
and other kidney disease (https://doi.org/10.1371/journal.pone.0233109 ). As mentioned, PGC1α negatively regulates TGFβ and is the main transcriptional regulator of mitochondrial biogenesis. It would be interesting (Fig-4a) to assess the mRNA levels using β-actin as a housekeeping gene.

Response:
We appreciate the reviewer's comment and we understand that, even though its mRNA expression was stable in our experiments, using Gapdh as housekeeping gene in the context of cell metabolism can be misleading. We therefore provided additional experiments and tested the ribosomal protein Rps12 (S12) and -actin (Actb) as housekeeping genes.
Based on these results and a previous study (Dahl SL et al. Acta Physiol., 2022 234 (3):e13768), we replaced Gapdh by S12/Actb as housekeeping genes in the qPCR analyses. Please find below, the comparison between these 3 selected housekeeping genes.

Response:
We thank the reviewer for this point. We spelled TGF- at the first mention in the introduction. Figure 1f, KIM-1 is measured by mRNA. It would be important to see protein levels of KIM-1.

Response:
We thank the reviewer for this comment. We provided KIM-1 immunofluorescence staining and quantification in the Extended Data Fig. 2. Figure 2a, the meaning of symbols "N", "BB", or the red arrows are not explained on the figure legend or anywhere in the manuscript.

Response:
We apologize for these omissions. We added the meaning and explanation of these symbols in Figure 2's legend.

10)
Line 208, authors mention mRNA and protein studies for PGC-1α, but did not reference "extended data figure 6".

Response:
We thank the reviewer for this remark. We referenced Extended Data

Response:
We appreciate the reviewer's comment and constructive critiques. We agree that several studies speculate on the role of systemic TGF- signaling on mitochondria and inflammatory response; however, our study is the first study that demonstrates with strong orthogonal validation how TGF- signaling affects the proximal tubule mitochondrial homeostasis and inflammatory in CKD. Our study reveals the potential role of S3T2 PT cells in post-injury inflammatory response. Moreover, our online database analysis suggests a possible role of intact PT TGF- signaling to ensure adaptive response to CKD in humans.
Specific critiques: 1) The abstract is general and fails to convey the experimentation conducted or results obtained. Humans and patients are mentioned in the abstract, but not mice, yet mouse and cell culture models constitute the bulk of the results.

Response:
We thank the reviewer for the remark and agree that the abstract failed to provide details on the experiments conducted and results. We rewrote the abstract accordingly. There are deconvolution methods which would facilitate colocalization analyses of the immune cell infiltrate with the various epithelial cell types.

Response:
We thank the reviewer for this important point. The Cloupe browser images in Fig.   1d provide an overall signal/intensity pattern of injury and fibrosis markers and validate the use of this technique to assess injury induction. These injury and fibrosis signals are confirmed in Fig.1f and Extended Data Figure 2 for Kim-1 (Havcr1 mRNA and immunofluorescence staining) and Fig.1h for collagen accumulation (Sirius red staining). We agree that a deconvolution could be run with the expression data of these individual cell types (single cell or sorted bulk cells). We chose integration over deconvolution methods in our spatial transcriptomics since the former are specifically designed to be robust to different noise models that characterize spatial and single-cell datasets, and have superior performance.
Therefore, each spot derived from the unsupervised clustering, the expression of marker genes described in the literature and the integration with the external single cell reference dataset GSE151658 using the 'anchor'-based integration workflow introduced in Seurat v3 (https://www.cell.com/cell/fulltext/S0092-8674(19)30559-8) that enables the probabilistic transfer of annotations from a reference to a query set. The output for each spot is a probabilistic classification for each of the scRNA-seq derived cell types, meaning that the reported celltype represents the dominant cell type at each spot. Figure 2 D/E -are these S3 PT's depicted?

Response:
We thank the reviewer for raising this important point. The live microscopy does not reach deep into the tissue (including outer medulla), so the S3 PT cells, especially those dwelling in the outer medulla, are not depicted. However, the proximal tubule is divided in two parts: the convoluted tubules (S1 and part of S2) dwelling in the cortex and the parse recta (part of S2 and S3) located at the cortico-medullary junction and in the outer medulla. i.e. S2 is the transition between S1 and S3. Recently, Polesel M et al., Nat Commun. 2022 (https://doi.org/10.1038/s41467-022-33469-5) revealed that the Ggt1 (gene) promoter driving Cre-expression in our mice is also active in the S2 segment, suggesting that not only S3 PT cells are concerned by Tgfbr2 deletion, but also part of S2 PT cells. Thus, the Figure 2 D/E depicts S1 and Ggt1 expressing proximal tubules including the transitional S2. Although Tgfbr2 is deleted in the cortico-medullary junction (part of S2 and S3 segment), showing the whole cortical proximal tubule adds a pathophysiological relevance to our findings, since increased vulnerability in the S3 segment (in the absence of Tgfbr2) can worsen S1 and S2 injury. Figure 3A how do you define PT specific spots? It would be helpful to add a couple feature plots of genes in these pathways there are detected in PT spots.

Response:
We thank the reviewer for this point. We included all cell types in Fig. 3A. PT specific feature plots are shown in Extended Figure 1. Please find below the dot plots that are included in the Extended Figure 1. Fig 5A -did Th1 inflammatory cells colocalize transcriptomically with PT S3 signature?

Response:
We thank the reviewer for raising this point. The S3 cluster (in the deep cortex) disappears under injury as we expected. The remaining part, the S3T2, dwells in the outer medulla and is involved in immune cell activation under injury. Based on cluster resolution on kidney slides, we can also observe the proximity between the S3T2 and clusters containing lymphocytes (Myo/St. mixed). However, one of the obvious limitations of spatial transcriptomics (Visium) is its low resolution (spot diameter is 55m). Although we can observe dot proximity, it is not proof for single cell proximity. However, we observed physical proximity of CD3+ positive T cells and PT cells in the cortico-medullary junction. Please refer to the image below. Fig 6B -the relevance of this is not apparent, please provide greater interpretation of these clusters Response: We thank the reviewer for this comment. We replaced Fig. 6b by a plot providing the percentage of PT cells per clusters in healthy and CKD biopsies. No significant difference in the overall PT cell proportion was found between healthy and clusters.

7)
Methodsplease provide the number of ST samples and spots. Pre-sequencing and Sequencing QC metrics should be provided Response: We thank the reviewer for this important remark and apologize for these omissions.
We added a supplementary table 3 with the requested information.

Reviewer #3
General comments: § This study updates our current understanding of the adaptability of TGF-ß signaling by shedding light on its role in mitochondrial homeostasis and renal immunity not only in the context of the presented kidney injury model but also by pointing to similarities to the human CKD phenotype. Hence, due to the high prevalence of CKD in our society and the importance of TGF-ß signaling in this context, the manuscript will be of great interest to the clinical and research community.

Response:
We appreciate the reviewer's comment. § The biochemical work involving conditionally immortalized proximal tubule cells is thoroughly and carefully carried out, documented, and presented. The same is the case for all histology and imaging experiments on mouse samples.

Response:
We appreciate the reviewer's comment. § In contrast, the transcriptomic experiments fall somewhat short. The reviewer appreciates that the authors highlight shortcomings in this area in the discussion section of their manuscript. Particularly, the use of single cell RNA sequencing (scRNAseq) data that stem from a biological experiment unrelated to the one pursued here are a concern as they were used to identify cell types in the spatial transcriptomics data, which formed the base of subsequent analyses and experimental steps. However, the observations made by the authors were substantiated by independent approaches they had carried out to test their hypotheses. Still, the chosen approach raises concerns. It would have been ideal if the authors could have generated scRNAseq data of their mouse model and used that as a source of single cell information. However, it is understood that such experiments are very expensive and hence not easily accessible. Spatial transcriptomics is an up-and-coming technology that has a lot to offer, and the authors made the best of it in terms of what was available to them.

Response:
We thank the reviewer for highlighting the limitations of the spatial transcriptomics that we did emphasize in the discussion section. We also appreciate that the reviewer understood that such experiments are very expensive and not easily accessible. However, we invested considerable effort to get best of it with regard to our assigned goals. While the spatial transcriptomics analyses could have been presented in a separate manuscript, we used it as a tool to point out the spatio-temporal insights in proximal tubule cells and immune cells interactions in late acute to chronic injury transition. § The transcriptomic experiments fall short for another reason. The manuscript provides only very sparse information on sample generation, source of data (no specifics to which of the data from GEO/Zenodo were used), and processing (how were the different datasets processed? How many cells went into the analysis? What percentage of doublets/mito/ribo transcripts were excluded? etc.). For example, it is not clear to the reviewer whether frozen or fixed samples were used for the Visium approach, what section thickness was chosen, how sections were selected etc. This technology is young and even less accessible to many laboratories. Hence, it is essential that details are provided at a level that is sufficient to allow the reader to understand the experimental design and to reproduce the data processing steps.
With regards to reproducibility, it is now common practice to provide key steps of data processing and visualization on open-access platforms such as Github.

Response:
We thank the reviewer for this important point. Regarding sample processing for the spatial transcriptomics experiments, we provided the pre-sequencing (sample processing) and sequencing information requested by the reviewer in the supplementary table 3 and in the online methods section "Samples processing for Spatial Transcriptomics". Regarding the human data sources, we provided additional information of the used dataset (accession number of the Zenodo repository is 4059315, Human scRNAseq on CD10+ cells, epithelial cells only, PMID 33176333) in the online method "snRNAseq Database Analysis". For snRNAseq data, Seurat v4.1.0 in R v4.2 was used for analyses, including normalization, clustering and differential expression. Firstly, we normalized, scaled and centered each sample separately using SCTransofrm v2. We further applied RunPCA, RunUMAP, FindNeighbours with default parameters, and FindCluster at a resolution of 0.5. Finally, counts were recorrected with PrepSCTFindMarkers for differential expression. Differential gene expression was evaluated using the Wilcoxon Rank Sum test from the FindMarkers function.
Feature plots were drawn using the plot_density function from the nebulosa package.
Calculation of the pathway scores was performed as previously described using REACTOME genesets. In this information is present in the online methods.
Minor comments: 1) Title: The authors might want to limit the statement to the model they investigated i.e., AA mouse model of CDK. Alternatively, the authors may want to mention the use of a mouse model in the Abstract.

Response:
We agree with the reviewer comment and mentioned the used injury model in the abstract.
2) The reviewer felt at times frustrated about the frequent reference to 'data not shown' in the manuscript.

Response:
We thank the reviewer for this comment and we apologize for this inconvenience.
We removed all "data not shown" and provided these data in the appropriate Extended Data Figures (2, 7 and 8) and Supplementary Figures (4, 5 and 8), with the exception of the glycolysis markers to keep the manuscript's focus.

3)
A bit more background information would be helpful to the non-initiated reader. For example, the AA model and the effects of AA on the kidney are not introduced. Not everybody is aware of the mito-QC mouse. What does it offer? Inconsistency when describing molecular players i.e., Pink1 is described but not LC3A-I.

Response:
We apologize for these omissions. We provided additional information on the aristolochic acid model and described the mito-QC reporter mouse and defined LC3A. Response: We thank the reviewer for raising these important points. We used 6 conditions and one animal per condition. Indeed, the goal of the spatial transcriptomics experiment is to complement our histological findings with spatio-temporal insights on tubulo-interstial interactions. Therefore, we focused on the GSE151658 dataset in which 8 animals (7 animals for single cell RNAseq and one animal was used for spatial resolution) to specifically identify cell types in our spatial transcriptomics data. After integrating spatial data with single-cell data by applying the 'anchor'-based integration workflow introduced in Seurat v3 (https://www.cell.com/cell/fulltext/S0092-8674(19)30559-8) that enables the probabilistic transfer of annotations from a reference to a query set, we had the choice to run Fisher's exact test or a Chi-Square test to indicate in the absence of sample replicates whether a certain cell type is overrepresented in a specific sample. Thus, we applied Fisher's Exact test to indicate whether a certain cell type is more or less abundant in GT-Cre;Tgfbr2 fl/fl sample relative to Tgfbr2 fl/fl as reflected by pValue and oddsRatio. We focused on male mice to follow up our previous study. A gender difference study is already foreseen. Fig 1a: at the resolution provided, the text overlayed onto the UMAP is not legible and, in the case of Myo/St.mixed, seems not to be in the correct location

Response:
We apologize for the inconvenience. We removed the text on the UMAP clusters, increased image resolution and kept cluster labelling only on the side legend. Fig 1b: The text overlayed on the spatial feature plot are not legible; the colors for S3 and Endo/Glom are hard to distinguish, especially with the H&E background. It is not clear which time point after aristolochic acid treatment is reflected here.

Response:
We thank the reviewer for raising this point. Fig1b depicts the impairment of cluster organization with regard to the renal anatomy layers. We used uninjured and 3 weeks AA injury kidneys. We increased the resolution of images. Fig 1c: colors do not match color scheme in panels a and b

12)
Response: We thank the reviewer for raising this point. We matched colors in panel a, b and c. Fig 1d: the resolution of the H&E and panels below is rather low; the Loupe browser images show horizontal bands with distortions Response: We thank the reviewer for this comment. The Cloupe browser images in Fig1d provide an overall signal/intensity pattern of injury and fibrosis markers and validate the technique with regard to injury/fibrosis induction. This signal pattern of injury and fibrotic markers is confirmed in Fig.1f and Extended Data Figure 2 for Kim-1 (Havcr1 mRNA and immunofluorescence staining) and Fig.1h for collagens (Sirius red staining).

14) Fig 1e: resolution is marginal
Response: We apologize for the marginal resolution. We increased the resolution of images in the Fig. 1e. Response: We thank the reviewer for raising this point. Indeed, Supplementary Fig. 1 shows the prediction score as computed by Seurat's function "TransferData". The label on the y-axis reading "Expression level" represent the "prediction score". The location of the clusters is indicated in the plot below, which comes from the 2021-12-13.Annotate_cellTypes.htm.

15) ED
Please find below the localization of the clusters in the UMAP as included in the Supplementary Fig. 1. Fig 1i, j: in both panels, for the Cre/fl.fl Tgfbr2 mice, there are two outliers. Do the data come from the same mice?

Response:
We thank the reviewer for raising this point. The outliers come from different mice; however, we added 4 Tgfbr2 fl/fl and 3 GTCre;Tgfbr2 fl/fl mice in Fig.1j to improve the statistical basis. Figure 2 and ED (Extended Data) Figure 2:

17)
-ED Fig 2b: color scheme makes it hard to identify data points that associate with the term 'Absent'

Response:
We thank the reviewer for this comment. We changed the color gray to dark red in the Extended Data Fig. 3 in the revised manuscript.

REVIEWERS' COMMENTS
Reviewer #1 (Remarks to the Author): This revised paper remains to be important.
The authors did a good job in revising the manuscript and responding the critiques.
I am left with the thought that this manuscript would better shortened and with more clarity.
Reviewer #2 (Remarks to the Author): For this re-submission, I will recap the summary of this manuscript: This manuscript by Kayhan et. al seeks to uncover how TGF-B signaling affects PT mitochondria function in CKD. The authors choose to tackle a well-worn topic, but do so with a tour de force approach, comprehensively defining the metabolic perturbations, transcriptomic alterations, and injury phenotypes of TGFBRII knockout in an aristolochic acid animal model. The authors perform additional validation in a proximal tubule cell culture model and draw upon existing clinical datasets to provide further corroboration of their results. This work builds upon their prior investigations in a 2017 JASN publication using the same murine model. The authors conduct a bevy of experiments and analyses: 1) histology and BUN, 2) immunofluorescence of immune cell infiltration and Pgc1a, 3) spatial transcriptomics, 4) use publicly available sc or sn RNAseq of murine and human kidneys including cell chat analyses 5) electron microscopy of mitochondria, 6) FACS for immune cell infiltration, 7) intravital imaging for dextran uptake, 8) Seahorse cell culture, 9) RNAseq of a PT cell culture model with TGFBRII knock out, 10) metabolic bioluminescence assays, 11) DCFDA cell assay, 12) RT-PCR and immunoblotting of key genes and proteins. The strengths of this investigation include strong orthogonal validation of their murine model with a vast array of techniques. The effect of TGFBR2 KO in a CKD model is comprehensively investigated, mixing recent technologies like spatial transcriptomics with classic ones. The potential of this manuscript has been nearly maximized.
The authors were mostly responsive and addressed about 40 critiques.
A major critique remains. The examination of mitochondrial dysfunction associated with TGF-beta in kidney disease has been well-studied and many of the techniques utilized in this manuscript have been previously published. The main advancement of science in this study is the inclusion of scRNAseq and spatial transcriptomics to study this pathway. However, the scRNAseq is not model specific and the spatial transcriptomics data are not analyzed at a state-of-the-art level that Nature Communications readers might expect. The authors show broad up-regulation of injury markers without niche analyses or deconvolution. I encourage the authors to examine this publication by Kuppe et. al (PMID: 35948637) to see how the Visium technology is leveraged to answer colocalization and niche questions. It would still be of significant interest to understand how immune cell infiltrate (like Th1 cells) localize with the various epithelial cell types (like PT S3).
As it stands, there may be insufficient justification for the use of Visium in this manuscript. The injury markers and pathways could have been appreciated in bulk sequencing. A novel injury spot or cell cluster is not identified. Further, there is no localization of injury. It does not seem quite enough to complete the wet lab portion of Visium, without the relevant analyses included as well.
Reviewer #3 (Remarks to the Author): The authors comprehensively addressed the reviewer's comments. The reviewer does not have any further concerns and recommends the manuscript for publication. In response to the reviewers' comments, the following changes have been made:

Reviewer #1
This revised paper remains to be important. The authors did a good job in revising the manuscript and responding the critiques. I am left with the thought that this manuscript would better shortened and with more clarity.

Response:
We appreciate the reviewer's comment. In order to comply with Nature Communications broad audience, we avoided usage of short technical words keeping the manuscript more detailed than it would be in a specialized journal.

Reviewer #2
For this re-submission, I will recap the summary of this manuscript: As it stands, there may be insufficient justification for the use of Visium in this manuscript. The injury markers and pathways could have been appreciated in bulk sequencing. A novel injury spot or cell cluster is not identified. Further, there is no localization of injury. It does not seem quite enough to complete the wet lab portion of Visium, without the relevant analyses included as well.

Response:
We thank the reviewer for this important comment. Mitochondrial dysfunction (mostly increased ROS production) associated with TGF-β has been reported in vitro; however the mechanisms whereby TGF-β affects mitochondrial function remained speculative in previous studies. Our study pointed out the dose-dependence effect of TGF-β on mitochondrial function such as lower doses promote oxidative phosphorylation whereas higher doses impair it; supporting the fact that intrinsic TGF-b signaling is beneficial in proximal tubule response to chronic renal injury. Most of in vitro data demonstrating the harmful effect of TGF-β used extremely high doses of TGF-β (10-30 ng/ml) leading to limited interpretation of TGF-β effect in these studies. In CKD, studies established association between high systemic TGF-β, renal fibrosis and mitochondrial dysfunction. In this study, we established how intrinsic TGF-β signaling modulates mitochondrial integrity and function under acute to chronic renal injury in the most metabolic and vulnerable renal segment (the proximal tubule).
Regarding the Transcriptomics, we agreed with the reviewer that not having generated our scRNAseq data constituted a limitation in spatial transcriptomics analysis including cell deconvolution. We therefore clearly stated this limitation in the discussion. Using integration Page 3/3

Institute of Physiology
over deconvolution methods in our spatial transcriptomics, the output for each spot is a probabilistic classification for each of the scRNA-seq derived cell types, meaning that the reported cell type represents the dominant cell type at each spot. The goal of the spatial transcriptomics was not to identify injury site which is clearly known in the field (that we can also determine by marker expression per annotated clusters), but to determine cell type/annotated cluster interaction using the CellchatDB. This procedure allowed us to point out enhanced interaction between the S3T2 cells and Macrophage and dendritic cell population in line with our FACS and IHC/immunofluorescence findings. Moreover, a separate spatial transcriptomics manuscript is already foreseen as a follow up of this study to uncover molecular contributors involved in the S3T2 and immune cell interaction.

Reviewer #3
The authors comprehensively addressed the reviewer's comments. The reviewer does not have any further concerns and recommends the manuscript for publication.

Response:
We thank the reviewer for the decision.